Method, system and storage medium for implementing intelligent question answering

ABSTRACT

Embodiments of the present invention provide a method, system and storage medium for implementing intelligent question answering. The method includes: receiving a query question; performing a semantic analysis of the question; performing corresponding search processing for the question based on a result of the semantic analysis, wherein the search processing includes search processing performed for the question by at least one of a semantic relationship mining system, a text library search system, a knowledge base search system, and a question and answer library search system; and returning an answer based on a result of the search processing. In this way, the accuracy of answers to the questions is effectively improved.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to Chinese Patent Application No.201510017563.6 filed by Baidu Online Network Technology (Beijing) Co.,Ltd. on Jan. 14, 2015, and entitled “Method and System for ImplementingIntelligent Question Answering,” which is incorporated herein byreference in its entirety.

TECHNICAL FIELD

Embodiments of the present invention relate to the field of informationquery technologies, and specifically to a method, system and storagemedium for implementing intelligent question answering.

BACKGROUND

An intelligent question answering (QA) system is an intelligent systembased on reasoning from massive Internet data and thorough semanticlanguage understanding. Intelligent QA systems not only can answerknowledge questions, but also can be applied to various fields closelyrelated to daily life, such as medical care, education, life, andscience and technology, significantly increasing the informationacquisition efficiency.

Existing intelligent QA systems are based mainly on questions andanswers in communities, and return a user's answer with high similarityafter mining historical data about users' questions and answers, andcalculating similarity between a user's question and existing questionsin a QA site.

The disadvantages of the above intelligent QA systems lie in that theintelligent QA systems relying on a QA site have low coverage ofquestions and cannot meet the users' requirement to answer moderately toless frequently and rarely asked questions. Answers provided by suchsystems are non-deterministic and not accurate enough.

SUMMARY

Embodiments of the present invention provide a method, system andstorage medium for implementing intelligent question answering, so as toimprove the accuracy of answers to the questions.

According to a first aspect, an embodiment of the present inventionprovides a method for implementing intelligent question answering, whichcomprises:

receiving a query question;

performing a semantic analysis of the question;

performing corresponding search processing for the question based on aresult of the semantic analysis, the search processing comprising searchprocessing performed for the question by at least one of a semanticrelationship mining system, a text library search system, a knowledgebase search system, and a question and answer library search system; and

returning an answer based on a result of the search processing.

According to a second aspect, an embodiment of the present inventionprovides a system for implementing intelligent question answering, whichcomprises:

a central control system for receiving a query question;

a question analysis system for performing a semantic analysis of thequestion,

the central control system being further configured to distribute, basedon a result of the semantic analysis, the question to a correspondingback-end system for corresponding search processing,

the back-end system comprising at least one of a semantic relationshipmining system, a text library search system, a knowledge base searchsystem, and a question and answer library search system,

the central control system being further configured to return an answerbased on a result of the search processing.

According to a third aspect, an embodiment of the present inventionprovides a non-volatile computer storage medium storing one or moremodules which when executed by a device performing a method forimplementing intelligent question answering, cause the device to performoperations comprising:

receiving a query question;

performing a semantic analysis of the question;

performing corresponding search processing for the question based on aresult of the semantic analysis, wherein the search processing includessearch processing performed for the question by at least one of asemantic relationship mining system, a text library search system, aknowledge base search system, and a question and answer library searchsystem; and

returning an answer based on a result of the search processing.

According to the method, system and storage medium for implementingintelligent QA that are provided in the embodiments of the presentinvention, a semantic analysis of a query question is performed, andbased on the semantic analysis, corresponding search processing isperformed for the question by using at least one of the semanticrelationship mining system, the text library search system, theknowledge base search system, and the question and answer library searchsystem, which broadens the search scope of answers to questions.Therefore, on one hand, answers to the question become more accurate; onthe other hand, the coverage of questions is expanded. Even formoderately to less frequently asked questions and rarely askedquestions, accurate answers can be obtained through search, so that theaccuracy of answers to the questions is effectively improved.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to better describe the technical solutions in the embodimentsof the present invention, the drawings used in the embodiments will bebriefly introduced below. It should be apparent that the drawings in thebelow description are merely embodiments of the present invention, andthose of ordinary skill in the art may amend and replace the drawingswithout creative efforts.

FIG. 1a is a schematic diagram of a network architecture of a system forimplementing intelligent question answering (QA) to which a method forimplementing intelligent QA of the embodiments of the present inventionis applicable;

FIG. 1b is a flow chart of a method for implementing intelligent QAaccording to a first embodiment of the present invention;

FIG. 2a is a flow chart of a method for implementing intelligent QAaccording to a second embodiment of the present invention;

FIG. 2b is a flow chart of establishing a ternary relationship libraryin the method for implementing intelligent QA according to the secondembodiment of the present invention;

FIG. 3 is a flow chart of performing search processing for a question bya text library search system in a method for implementing intelligent QAaccording to a third embodiment of the present invention;

FIG. 4 is a flow chart of returning an answer according to a searchprocess result in a method for implementing intelligent QA according toa fourth embodiment of the present invention;

FIG. 5 is a schematic structural diagram of a system for implementingintelligent QA according to a fifth embodiment of the present invention;and

FIG. 6 is a schematic structural hardware diagram of a device forexecuting a method for implementing intelligent QA according to aseventh embodiment of the present invention.

DETAILED DESCRIPTION

The technical solutions of the embodiments of the present invention willbe described clearly and completely below with reference to theaccompanying drawings. It should be apparent that the describedembodiments are some embodiments rather than all embodiments of thepresent invention, used for explaining the principle of the presentinvention, and not intended to limit the present invention to thesespecific embodiments. On the basis of the embodiments in the presentinvention, all other embodiments acquired by those of ordinary skill inthe art without creative work fall within the scope of the presentinvention.

The methods of the embodiments of the present invention may be executedby a system for implementing intelligent QA that is configured to beimplemented using hardware and/or software.

For the purpose of clarity, the network architecture of the system isintroduced below with reference to FIG. 1a . The system includes: acentral control system, a question analysis system, and a back-endsystem.

The central control system is used for receiving a query question (i.e.,query in FIG. 1a ). The central control system may receive, through apre-defined text box, a query question that is input by a user, or mayreceive, through a pre-defined action button, a query question that isinput by the user. For example, the central control system receives,through an action button for receiving a user voice, a voice that isinput by the user, and performs voice recognition to obtain contentcorresponding to the input voice, which is used as the query question.The question analysis system is used for performing a semantic analysisof the question. The central control system is further used fordistributing, based on a result of the semantic analysis, the questionto a corresponding back-end system for corresponding search processing.The back-end system includes at least one of the following systems: asemantic relationship (Frame) mining system, a text library searchsystem, a knowledge base search system, and a question and answerlibrary search system. The central control system is further used forreturning an answer based on the result of the search processing (i.e.,the result after merging in FIG. 1a ).

It should be noted that the central control system is the core controlsystem of the system for implementing intelligent QA, and is responsiblefor receiving the query question; forwarding the question to thequestion analysis system; distributing, based on the result of thesemantic analysis of the question analysis system and on the basis of apre-set distribution policy, the question to a relevant back-end systemfor corresponding search processing, specifically to at least one of thesemantic relationship mining system, the text library search system, theknowledge base search system, and the question and answer library searchsystem for corresponding search processing; and returning an answerbased on the result of the search processing, so as to enable afront-end user of the central control system to know the answercorresponding to the query question, thereby implementing intelligentQA.

Different back-end systems correspond to different resource libraries,which will be described in details in the following embodiments. Inaddition, the distribution policy will also be described hereinafter.

First Embodiment

Referring to FIG. 1b , a method for implementing intelligent QA providedby this embodiment includes: operations 110 to 140.

In the operation 110, a query question is received.

In this operation, the central control system receives a query question.The central control system may receive, through a text box that ispre-defined in a user interface, a query question that is input by auser, or may receive, through an action button that is pre-defined in auser interface, a query question that is input by the user. For example,the central control system receives, through an action button forreceiving a user voice, a voice that is input by the user, and performsvoice recognition to obtain content corresponding to the input voice,which is used as the query question.

It should be noted that the received query question may havecomprehensive coverage. In other words, the received query question maybe any question, i.e. the received query question may be a question ofwhich the frequency of occurrence is relatively high and that is ofinterest to the user, or may be a question of which the frequency ofoccurrence is moderate or to which the answers provided by the currentQA system are not satisfactory to the user, and may also cover rarelyasked questions, i.e., questions of which the frequency of occurrence isvery low.

In the operation 120, a semantic analysis of the question is performed.

This operation is performed by the question analysis system, and a basiclexical analysis of the question may be performed. The basic lexicalanalysis may include performing segmentation processing andpart-of-speech-tagging processing on the question, and may furtherinclude determining the importance of a search term in the question,performing a dependency syntax analysis of the question, etc.

Dependency syntax was first proposed by a French linguist L. Tesniere inhis work Elements of Structural Syntax in 1959, which has a profoundeffect on the development of linguistics. Dependency syntax analysis isan important branch of the syntax analysis in the natural languageprocessing field. The dependency syntax analysis is to analyze asentence to create a dependency syntax tree and describe the dependencyrelationship between phrases so as to reveal the syntactic structure ofthe sentence, and advocates that: the core verb in the sentence is thecentral element governing other elements, the core verb itself is notgoverned by any other element, and all the governed elements aresubordinate to the governor on a certain dependency relationship.

In this operation, in addition to performing the basic lexical analysisof the question, the method may further preferably include identifying afocus, an opinion, and a type of the opinion of the question.

The focus of the question is a reference to the answer to the question,and may replace the answer to form a complete declarative sentence. Forexample, in the question “Who is the 2013 Wimbledon Women's Singleschampion,” the focus is “who”. The focus of the question is mainlyidentified by using a rule, where the identification rule may be that ifthe question is a question with an interrogative, the focus of thequestion is the interrogative; or if the question is a question withoutan interrogative, the focus of the question is empty by default.

Identifying the opinion of the question refers to identifying the numberof opinions of the question, i.e., the number of opinions contained inthe question. The number of opinions of the question may be a generalrequirement. The number of opinions of a general requirement isgenerally greater than eight, or may be a specific number. For example,the number of opinions of the question “What are wild animals” is tensof thousands, respectively corresponding to different wild animals, andthe opinion of the question is defined as a general requirement. Foranother example, the number of opinions of the question “Four Beautiesin Ancient China” are four, because “Four” is mentioned in the question.As yet another example, the number of opinions of the question “Who isthe 2013 Wimbledon Women's Singles champion” is one, because thechampion is a single person in the context of the current question.

Identifying the type of the opinion of the question refers toidentifying the type of the opinion that is needed by the user. Forexample, for the question “Who is the 2013 Wimbledon Women's Singleschampion,” the type of the opinion of the question is “champion.”Methods for identifying the type of the opinion of the question areclassified into two categories: rule-based identification methods andmodel learning models. In the rule-based identification methods, thetype of the opinion is the word previous to the focus; in the modellearning models, tools such as dependency syntax analysis are used as abasis to generate a training corpus and training model dynamics, so asto identify the type of the opinion of the question.

The method may further include, after the opinion type of the questionis identified: performing normalization processing on the type of theopinion of the question.

Normalization of the type of the opinion of the question is to map thetype of the opinion of the question to a fixed category system. Forexample, the question “Who is the 2013 Wimbledon Women's Singleschampion,” of which the focus is “who” and the type of the opinion is“champion,” is normalized to the category “person.” Methods fornormalizing the type of the opinion of the question are generally basedon an opinion rule or an opinion vocabulary.

In the operation 130, corresponding search processing is performed forthe question based on the result of the semantic analysis, wherein thesearch processing includes search processing performed for the questionby at least one of the semantic relationship mining system, the textlibrary search system, the knowledge base search system, and thequestion and answer library search system.

As described above, this operation is executed by the central controlsystem and a back-end system. Specifically, based on the result of thesemantic analysis of the question analysis system and on the basis of apre-set distribution policy, the question is distributed to acorresponding back-end system for corresponding search processing,specifically to at least one of the semantic relationship mining system,the text library search system, the knowledge base search system, andthe question and answer library search system for corresponding searchprocessing.

The various back-end systems described above may be used separately, ormay be used in combination.

The distribution policy based on which the corresponding searchprocessing is performed for the question based on the result of thesemantic analysis may include:

performing search processing for the question by the semanticrelationship mining system if the analyzed question has a structuraldependency type (i.e., “previous sentence/next sentence” type);otherwise, performing search processing for the question by the textlibrary search system, the knowledge base search system, and thequestion and answer library search system.

It should be noted that the search processing performed by the variousback-end systems for the question is independent of each other.

Resource libraries corresponding to different back-end systems will bedescribed below.

A resource library of the semantic relationship mining system mayinclude two parts: an inverted index library, and a Kv (key value)resource library, both of which may be constructed by using an offlinedocument processing program. Generally, there are many documents in adocument set that contain a certain word, and each document recordsinformation such as a document number, the number of occurrences of theword in the document, and positions at which the word appears in thedocument. Such information related to one document is referred to asinverted indexes. The search efficiency can be improved by usinginverted indexes. The Kv resource library may contain mined poemresources, lyric resources, etc., and is used for processingdependency-type questions.

A resource library of the text library search system may include: a textlibrary and a Kv resource library. The text library is established onthe basis of the search engine technology to index, analyze and rankmassive non-structured web pages, and based on the massivenon-structured web page data. The Kv resource library is establishedbased on libraries such as Baidu Baike and Wikipedia. Because massivenon-structured web page data and the libraries such as Baidu Baike andWikipedia contain extensive knowledge, the text library search system isthe core of the entire back-end system, that is, a core processingsystem for implementing intelligent QA, which can implement the searchof answers to the query question that is input by the user.

A resource library of the knowledge base search system may be astructured offline mining knowledge base, which is stored in the form ofa triplet <entity, attribute, value>, for example, <Zhang San, wife,beautiful girl>, <beautiful girl, constellation, Aries>, or <Avatar,author, James Cameron>, and is responsible for reasoning on the basis ofinformation in the knowledge base. For example, for the question “Whatis the constellation of Zhang San's wife?” it can be reasoned from ZhangSan's wife being a beautiful girl and the constellation of a beautifulgirl being Aries that the answer to the question is “Aries.”

A resource library of the question and answer library search system maybe a QA community resource library formed on the basis of offline minedhigh-quality QA data, for example, historical questions and answers ofusers in Baidu Zhidao. The processing procedure of the question andanswer library search system is similar to that of the text librarysearch system, and the specific processing procedure of the text librarysearch system will be described in details in the following thirdembodiment.

In the operation 140, an answer is returned based on the result of thesearch processing.

According to the technical solution of this embodiment, a semanticanalysis of a query question is performed, and based on the semanticanalysis, corresponding search processing is performed for the questionby using at least one of the semantic relationship mining system, thetext library search system, the knowledge base search system, and thequestion and answer library search system, which broadens the searchscope of answers to questions. Therefore, on one hand, answers to thequestion become more accurate; on the other hand, the coverage ofquestions is expanded. Even for moderately to less frequently askedquestions and rarely asked questions, accurate answers can be obtainedthrough search, so that the accuracy of answers to the questions iseffectively improved.

The method for implementing intelligent QA that is provided by thisembodiment of the present invention may be executed by a cloud system,embedded in a robot of any external shape, and is applicable to any QAscenario. For example, the method is applicable to tutoring: studentsencountering unfamiliar knowledge points can directly communicate with asystem for implementing intelligent QA to acquire more comprehensive,real and useful information, for example, “Who proposed the Law ofGravity? What is the meaning of the proposal of the Law of Gravity.” Foranother example, the method is applicable to providing an automaticcustomer service in public places: when a customer is in a shopping mallor is choosing clothes, she interacts with a system for implementingintelligent QA to learn which one of natural fiber and synthetic fiberis better and learn their respective advantages and disadvantages; orwhen visiting a park, a tourist interacts with a system for implementingintelligent QA to learn information such as the flowering season ofroses. For yet another example, the method is applicable tosupplementary medical treatment: combining big data on the Internet andcase information of users, and through analysis and reasoning by using asystem for implementing intelligent QA, an etiological analysis isprovided for reference.

Exemplarily, performing corresponding search processing for the questionbased on the result of the semantic analysis includes:

performing search processing for the question by the semanticrelationship mining system if the analyzed question has a structuraldependency type; otherwise, performing search processing for thequestion by the text library search system, the knowledge base searchsystem, and the question and answer library search system.

Exemplarily, performing search processing for the question by thesemantic relationship mining system includes:

searching the ternary relationship library for an answer to thequestion.

Exemplarily, the ternary relationship library is established by:

performing a grammatical analysis of an original web page in a networkto obtain a sentence having a dependency relationship;

extracting nodes of the dependency relationship to obtain a ternaryrelationship triple; and

validating reasonableness of the ternary relationship triple, andestablishing the ternary relationship library by using the ternaryrelationship triples the reasonableness of which is validated.

Exemplarily, the method further includes, before the sentence having thedependency relationship is obtained:

removing content in brackets in the sentence.

Exemplarily, the method further includes, before the ternaryrelationship triple is obtained:

adding at least one of a subject, an adverbial, and an object that thesentence having the dependency relationship lacks.

Exemplarily, performing search processing for the question by the textlibrary search system includes:

performing a search to obtain a document set related to the question;

searching the document set for a candidate segment, wherein thecandidate segment includes a candidate answer; and

extracting the candidate answer from the candidate segment, andperforming a first ranking operation.

Exemplarily, performing the first ranking operation includes:

performing ranking based on at least one feature of a uniform resourcelocator (URL) weight, an offset weight, and a question matching degree,wherein the URL weight is a weight of a URL link and a site to which adocument where the candidate answer is located belongs, the offsetweight is a distance coefficient of the candidate answer with respect toa keyword in the question in different candidate segments, and thequestion matching degree is a semantic matching degree between thecandidate answer and the type of the opinion of the question.

Exemplarily, returning the answer based on the result of the searchprocessing includes:

filtering the result of the search processing by using intra-domainknowledge; and

returning the answer based on the result of the filtering.

Exemplarily, returning the answer based on the result of the filteringincludes:

performing a second ranking operation of the result of the filtering;and

returning the ranked result of the filtering.

Exemplarily, performing the second ranking operation of the result ofthe filtering includes:

ranking the result of the filtering by using a supervised machinelearning model.

Exemplarily, returning the answer based on the result of the filteringincludes:

performing a third ranking operation of the result of the filtering; and

returning the ranked result of the filtering.

Exemplarily, performing the third ranking operation of the result of thefiltering includes:

validating the matching degree between the candidate answer and thequestion; and

ranking the candidate answers based on the matching degrees.

Second Embodiment

On the basis of the above embodiment, this embodiment provides atechnical solution of another method for implementing intelligent QA.

Referring to FIG. 2a , the method for implementing intelligent QAprovided by this embodiment specifically includes: operations 210 to240.

In the operation 210, a query question is received.

In the operation 220, a semantic analysis of the question is performedto know that the type of the opinion of the question is “previoussentence” or “next sentence.”

This operation is also applicable to the operation of performing a basiclexical analysis of the question and the operation of identifying afocus, an opinion, and a type of the opinion of the question in theforegoing embodiment, which will not be repeatedly described herein.

In the operation 230, the semantic relationship mining system searchesthe ternary relationship library for an answer to the question.

In this operation, ternary relationships in the ternary relationshiplibrary encompass dependency relationships using a verb as the core,dependency relationships between entities and attributes and dependencyrelationships between different entities, and the ternary relationshipsin the ternary relationship library have passed a reasonablenessvalidation.

Referring to FIG. 2b , the ternary relationship library is establishedpreferably by: operations 231 to 236.

In the operation 231, preprocessing is performed.

Content in brackets such as “( )” in sentences is removed, so as toavoid affecting the structure of dependency syntax analysis.

In the operation 232, a basic lexical/grammatical analysis is performed.

Segmentation, proper noun recognition, entity recognition,part-of-speech-tagging and dependency analysis may be performed on asentence part contained in the text in an original web page, so as toobtain a sentence having a dependency relationship.

In the operation 233, adverbial segment identification is performed.

For example, segment type identification is performed on segmentsseparated by commas, which are mainly classified into time adverbialsegments, non-time adverbial segments and other segments.

In the operation 234, ternary relationship triples are extracted.

Specifically, this operation is to perform relationship extraction basedon each parent node of dependency analysis, to obtain ternaryrelationship triples. The ternary relationship triples specificallyinclude: dependency relationships using a verb as the core, dependencyrelationships between entities and attributes and dependencyrelationships between different entities. For example, in the sentence“Student A wins an award,” between “Student A” and “award” there is adependency relationship using a verb as the core; in the sentence“Huangshan Mountain is very beautiful,” between “Huangshan Mountain” and“very beautiful” there is a dependency relationship between the entityand the attribute; the next sentence after “The sun beyond the mountainsglows” is “The Yellow River seawards flows,” which may be construed as adependency relationship between different entities.

In the operation 235, a missing element is added.

For example, a subject, a time/location adverbial, or an object isadded.

In the operation 236, reasonableness validation and filtering areperformed.

It is determined whether the ternary relationship triples obtainedthrough analysis are reasonable. If a ternary relationship triple is notreasonable, the ternary relationship triple will not be kept. That is,filtering is performed on the ternary relationship triples obtainedthrough analysis, so that only ternary relationship triples that arereasonable, i.e., ternary relationship triples that have passed thevalidation, are kept. Then, the ternary relationship library isestablished by using the ternary relationship triples that have passedthe validation.

For example, “The Yellow River seawards flows, the sun beyond themountains glows” is extracted from an original web page, and it has beenobtained in the operation 233 through extraction according to theoriginal web page that “The Yellow River seawards flows” is the previoussentence of “the sun beyond the mountains glows.” In this case, it canbe determined based on existing poem resources that this ternaryrelationship triple is not reasonable, and therefore, the ternaryrelationship triple is filtered out.

In the operation 240, an answer is returned based on the result of thesearch processing.

In the technical solution of this embodiment, a semantic analysis of aquery question is performed, and based on the semantic analysis,corresponding search processing in a ternary relationship library isperformed for the question by using the semantic relationship miningsystem, so as to find an answer to the question. Because ternaryrelationships in the ternary relationship library encompass dependencyrelationships using a verb as the core, dependency relationships betweenentities and attributes and dependency relationships between differententities, the search scope of answers to questions is increased, andtherefore the coverage of questions is expanded. Even moderately to lessfrequently asked questions and rarely asked questions can be covered. Inaddition, because the ternary relationships in the ternary relationshiplibrary have passed reasonableness validation, answers to questionsbecome more accurate.

It should be noted that in the process of setting up the ternaryrelationship library, the method may further include, before the ternaryrelationship triples are obtained: adding at least one of a subject, anadverbial, and an object that the sentences having the dependencyrelationship lack.

Specifically, segment types of the sentences having the dependencyrelationship may be identified, which include: subjects, predicates,objects, adverbials, etc., where the adverbials may be furtherclassified into the following types: time adverbials, locationadverbials, reason adverbials, and result adverbials. Verbs act as thepredicates. As described above, the dependency syntax analysis is toanalyze a sentence to obtain a dependency syntax tree so as to describethe dependency relationship between phrases, and thus the syntacticstructure of the sentence is revealed. The dependency syntax analysisadvocates that: the core verb in the sentence is the central elementgoverning other elements, the core verb itself is not governed by anyother element, and all the governed elements are subordinate to thegovernor on a particular dependency relationship. The dependencyrelationship using a verb as the core refers to a dependencyrelationship between the predicate and segments of other types.

In this manner, adding other elements than the predicates that thesentences having the dependency relationship lack can help enrichdependency relationships using a verb as the core in the ternaryrelationships, thereby further broadening the search scope of answers toquestions, and further expanding the coverage of questions. Evenmoderately frequently and less frequently asked questions and rarelyasked questions can be covered.

Third Embodiment

On the basis of the first embodiment, in this embodiment, the operationof performing corresponding search processing for the question based onthe result of the semantic analysis is optimized to be: performingsearch processing for the question by the text library search systembased on the result of the semantic analysis.

Referring to FIG. 3, a flowchart of performing search processing for aquestion by a text library search system in a method for implementingintelligent QA according to a third embodiment of the present inventionis shown. The method specifically includes: operations 310 to 330.

In the operation 310, a related document is searched for.

A search is performed in a resource library to obtain a search result, aweb page text, and click logs, and a document set related to thequestion is obtained through the search.

In this operation, the question may be searched for by using a searchengine, to retrieve a related web page set as a document set related tothe question. Correlation ranking by the search engine reflects theimportance level of each document to some extent. Specifically, thequestion may be searched for by using the search engine, to obtainsearch results and other resources related to the question which mayinclude a digest, a Uniform Resource Locator (URL), document click data,document text information, etc. Then, document content corresponding toa web page is acquired and retrieved according to the URL, which is usedfor performing a deep analysis of the questions and answers. Thesatisfaction level of each document for the question is analyzed throughclick logs.

The click log is used to evaluate, based on the level of the URL, thesatisfaction level of each clicked document for the question.

The method may further include performing preprocessing, to mergeresources that are substantially the same but are expressed usingdifferent methods, for example, time “March 2010” and “2010. 03;” and tocorrect resources that are incorrectly expressed.

Preferably, the method may further include document correlationcalculation and document ranking.

The document correlation may be calculated based on features such assemantic similarity. Calculation of the semantic similarity refers tocalculating the value of similarity between the question and documenttitles. The method for semantic calculation mainly uses information,such as, the importance of the search term, replacement of the searchterm with synonyms, modification of the search term, etc., forcalculation.

The document correlation ranking refers to that the search engineretrieves a large number of web page documents based on the search termin the question without limiting its escape risk. In the documentcorrelation ranking method, correlation re-ranking is performed based onthe search and ranking result of the search engine (for example, Baidusearch engine) and the user satisfaction levels of web pages analyzed inthe click logs, and in combination of features such as semanticsimilarity calculation, so as to solve the content escape problem.Content escape may introduce noise data, resulting in extraction ofincorrect answers or in that incorrect answers have a high ranking inthe ranking phase.

In the operation 320, candidate segments are extracted and ranked.

The document set is searched for candidate segments, wherein thecandidate segments include candidate answers.

First, each document may be segmented. The document segmentationgenerally takes paragraph or multiple sentences as a unit. Then, a setkeyword is searched for in each document so as to obtain segmentscorresponding to the set keyword as candidate segments, which arefurther used for locating candidate answers.

The candidate segments may further be ranked. Specifically, theconfidence of the candidate segment is calculated based on features suchas the correlation weight of a source document, content similarity ofthe candidate segment, and correlation of the candidate segment, and thecandidate segments are ranked based on the confidence.

The correlation weight of the source document may be calculated bylinear fitting of related features of document correlation re-ranking.The content similarity of the candidate segment is used for calculatingcontent similarity between the question and the candidate segment, andmay be calculated by using the semantic similarity calculation method.Calculation of the correlation of the candidate segment is used formeasuring the correlation between the question and the candidatesegment, and the calculation method is mainly to fit features such asthe importance of each search term in the question, the number of hitsand the positions of hits of each search term in the candidate segment,etc.

In the operation 330, candidate answers are extracted and ranked.

The candidate answers are extracted from the candidate segments, and afirst ranking operation is performed.

In this operation, the operation of extracting candidate answers may beimplemented by means of the named entity recognition technology using anoffline mined open-domain dictionary. The open-domain dictionary is acomprehensive dictionary.

The first ranking operation is preferably to perform ranking based on atleast one feature of a URL weight, an offset weight, and a questionmatching degree, wherein the URL weight is a weight of a URL link and asite to which a document where the candidate answer is located belongs,the offset weight is a distance coefficient of the candidate answer withrespect to a keyword in the question in different candidate segments,and the question matching degree is a semantic matching degree betweenthe candidate answer and the type of the opinion of the question.

Alternatively, the first ranking operation may be performed on thecandidate answers based on co-occurrence weights of the candidateanswers and the keyword in the question.

In addition, the first ranking operation may alternatively be performedon the candidate answers based on at least one of the followingfeatures: the position of occurrence of the candidate answer in thecandidate segment, inverse document frequency of the candidate answer inthe search result, the correlation of the source document, thecorrelation of a source candidate segment, and confidence-weightedvoting weights of different candidate segments for a same answer(including answers having a same meaning).

The correlation of the source document may be obtained by linear fittingof related features of the document correlation re-ranking; thecorrelation of the source candidate segment may be obtained by linearfitting of the correlation of the source document and the semanticmatching degree between the source segment and the question.

The first ranking operation on the candidate answers is mainly for thepurpose of ensuring the retrieve of the candidate answers, and avoidingexcessive answers to be ranked in a second ranking operation affectingthe ranking performance and noise control.

It should be noted that the method may further include, after thecandidate answers are extracted and ranked: further ranking thecandidate answers, for example, filtering by using intra-domainknowledge, a second ranking operation, a third ranking operation, whichwill be described in details in conjunction with the followingembodiments.

Fourth Embodiment

On the basis of the above embodiments, this embodiment provides apreferable solution for the operation of returning an answer based onthe result of the search processing.

Referring to FIG. 4, a flow chart of returning an answer based on theresult of search processing in a method for implementing intelligent QAaccording to the fourth embodiment of the present invention is shown.The method specifically includes: operations 410 to 420.

In the operation 410, the result of the search processing is filtered byusing intra-domain knowledge.

Knowledge, features and ranking algorithms required by differentquestions and different types of answers differ. Therefore, the resultof the search processing may be filtered by using knowledge of differentdomains, to obtain a search processing result matching the question.

Construction of domain knowledge is mainly construction of a precisedomain knowledge dictionary. For example, golden retriever dog is a dog,and black dragon eye goldfish is a fish. If the type of the question isdog, the candidate answer “black dragon eye goldfish” will be filteredout by the domain dictionary.

Construction of the domain dictionary is mainly implemented by usingalgorithms such as site-oriented structured data mining (for example,mining novel entities from qidian.com), large-scale Internetunstructured/semi-structured data mining and verifying (for example,mining entities by using classification labels of a knowledge-typecommunity such as Baike), or search logs mining (for example, miningDemi-Gods and Semi-Devils movie entities from search logs correspondingto the question “Demi-Gods and Semi-Devils Movie”).

In the operation 420, the answer is returned based on the result of thefiltering.

This operation may have multiple implementations, and is described byusing the following implementations as examples.

In a first implementation, returning the answer based on the result ofthe filtering specifically includes:

performing a second ranking operation of the result of the filtering;and

returning the ranked result of the filtering.

The second ranking operation may be performed on the result of thefiltering by using a supervised machine learning model (for example, theGBRank machine learning model).

The machine learning model is obtained by learning and trainingstatements containing ranked sample answers and corresponding rankingfeatures, and may include at least one of the following rankingfeatures: a question matching degree, offset weight, question-answerco-occurrence information, an answer boundary feature, and answerconfidence.

The question matching degree is a semantic matching degree between thecandidate answer and the type of the opinion of the question. Thequestion matching degree is usually calculated by fitting the matchingdegree between an extended vector of the question and an extended vectorof a candidate answer that are counted from a large scale corpus. Theoffset weight is a distance coefficient of the candidate answer withrespect to a keyword in the question in different candidate segments.The question-answer co-occurrence information is used for measuring theimportance level of the question in the primary context of the candidateanswer. For the calculation of the entity-type question-answerco-occurrence information, the degree of co-occurrence may be calculatedby using information of the candidate answer in Baidu Baike and thekeyword in the question. The answer boundary feature refers toinformation about the left and right boundaries of the answer, forexample, guillemet, quotation marks, or a Chinese back-sloping comma.The answer confidence is related to the source document. Each documentis considered a source of evidence, and has one vote. Each sourcedocument votes once every time. Theoretically, a document with morevotes has a higher answer confidence. Alternatively, the answerconfidence may be determined based on the candidate segment to which theanswer belongs. The document to which individual candidate segmentbelongs is considered a source of evidence and has one vote, and theweight of each vote of each source is determined by the correlation ofthe candidate segment. Theoretically, a document with more votes has ahigher answer confidence.

This implementation may be used alone, or may be used in combinationwith the foregoing first ranking operation. Preferably, after thecandidate answers are extracted from the candidate segments, the firstranking operation is performed, and the filtering operation is carriedout by using intra-domain knowledge, the second ranking operation isperformed on the result of the filtering.

In this preferable implementation, after the candidate answers areextracted from the candidate segments, the first ranking operation isperformed by using at least one feature of the URL weight, the offsetweight, and the question matching degree, thereby improving the accuracyof top ranked answers; answers corresponding to different categories ofquestions are filtered by using professional domain knowledge, therebyensuring the professionalism and authority of answers corresponding todifferent categories of questions; the second ranking operation isperformed on the result of the filtering by using at least one featureof the question matching degree, the offset weight, the question-answerco-occurrence information, the answer boundary feature and the answerconfidence, thereby further improving the accuracy of top ranked answerswhile ensuring the professionalism and authority of the answerscorresponding to the questions.

In a second implementation, returning the answer based on the result ofthe filtering specifically includes:

performing a third ranking operation on the result of the filtering; and

returning the ranked result of the filtering.

Further, performing the third ranking operation on the result of thefiltering preferably includes:

validating a matching degree between the candidate answers and thequestion; and

ranking the candidate answers based on the matching degree.

Specifically, a secondary search may be used to validate the matchingdegree between the candidate answers and the question. The secondarysearch can provide richer information about match between the questionand the answer, and refers to replacing the focus of the originalquestion with the candidate answer, i.e., substituting the candidateanswer into the original question to perform a secondary search, andcollecting statistics on relevant information about the new question andthe candidate answer, for example, whether the new question and thecandidate answer appear successively in a document returned by thesecondary search, co-occurrence information of the new question and thecandidate answer, or information about hits of the keyword in the newquestion. If the title of a current document is a question-type title,the focus and the answer type are identified from the title of thedocument. If the question has no focus, the answer is added to the endof the original question and separated by a separator, and then thesecondary search is performed.

This implementation may be used alone, or may be used in combinationwith the foregoing first ranking operation and/or the foregoing secondranking operation.

When this implementation is used alone, because the candidate answer issubstituted into the original question to form a new question for whicha secondary search is performed, the order in which the answers arearranged is optimized based on the relevant information about the newquestion and the candidate answer, so that the matching degree betweentop ranked answers and the original query question can be improved.

After the candidate answers are extracted from the candidate segments,the first ranking operation is performed, and the filtering operation isexecuted by using intra-domain knowledge, the second ranking operationand the third ranking operation are performed on the result of thefiltering. In this implementation, after the candidate answers areextracted from the candidate segments, the first ranking operation isperformed by using at least one feature of the URL weight, the offsetweight, and the question matching degree, thereby improving the accuracyof top ranked answers; answers corresponding to different categories ofquestions are filtered by using professional domain knowledge, therebyensuring the professionalism and authority of the answers correspondingto different categories of questions; the second ranking operation isperformed on the result of the filtering by using at least one featureof the question matching degree, the offset weight, the question-answerco-occurrence information, the answer boundary feature and the answerconfidence, thereby further improving the accuracy of top ranked answerswhile ensuring the professionalism and authority of the answerscorresponding to the questions; the candidate answer is substituted intothe original question to form a new question for which a secondarysearch is performed, and the order in which the answers are arranged isoptimized based on the relevant information about the new question andthe candidate answer, thereby further improving the matching degreebetween top ranked answers and the original query question.

Fifth Embodiment

This embodiment provides a system for implementing intelligent QA.Referring to FIG. 5, the system includes: a central control system 510,a question analysis system 520, and a back-end system 530.

The central control system 510 is configured to receive a queryquestion; the question analysis system 52 is configured to perform asemantic analysis of the question; the central control system 510 isfurther configured to distribute, based on a result of the semanticanalysis, the question to the corresponding back-end system 530 forcorresponding search processing; the back-end system 530 includes atleast one of the following systems: a semantic relationship miningsystem, a text library search system, a knowledge base search system,and a question and answer library search system; and the central controlsystem 510 is further configured to return an answer based on a resultof the search processing.

According to the technical solution of this embodiment, a semanticanalysis of a query question is performed, and based on the semanticanalysis, corresponding search processing is performed for the questionby using at least one of the semantic relationship mining system, thetext library search system, the knowledge base search system, and thequestion and answer library search system, which broadens the searchscope of answers to questions. Therefore, on one hand, answers for thequestion become more accurate; on the other hand, the coverage ofquestions is expanded. Even for moderately to less frequently askedquestions and rarely asked questions, accurate answers can be obtainedthrough search.

In the above mentioned solution, the question analysis system 520 may bespecifically configured to: identify a focus, an opinion, and a type ofthe opinion of the question.

Further, the question analysis system 520 may be further configured to,after identifying the type of the opinion of the question: performnormalization processing on the type of the opinion of the question.

In the above mentioned solution, the central control system 510 may bespecifically configured to: distribute the question to the semanticrelationship mining system for search processing if the analyzedquestion has a structural dependency type; otherwise, distribute thequestion to the text library search system, the knowledge base searchsystem, and the question and answer library search system for searchprocessing.

The semantic relationship mining system may be specifically configuredto: search a ternary relationship library for an answer to the question.

The semantic relationship mining system may be further configured to:

perform a grammatical analysis of an original web page in a network toobtain a sentence having a dependency relationship;

extract nodes of the dependency relationship to obtain a ternaryrelationship triple; and

validate reasonableness of the ternary relationship triples, andestablish the ternary relationship library by using the ternaryrelationship triple the reasonableness of which is validated.

The semantic relationship mining system may be further configured to,before obtaining the sentence having the dependency relationship: removecontent in brackets in the sentence.

The semantic relationship mining system may be further configured to,before obtaining the ternary relationship triple: add at least one of asubject, an adverbial, and an object that the sentence having thedependency relationship lacks.

The text library search system may be specifically configured to:

perform a search to obtain a document set related to the question;

search the document set for candidate segments, wherein the candidatesegments include candidate answers; and

extract the candidate answers from the candidate segments, and perform afirst ranking operation.

The text library search system may be specifically configured to:perform ranking based on at least one feature of a URL weight, an offsetweight, and a question matching degree, wherein the URL weight is aweight of a URL link of a site to which a document where the candidateanswer is located belongs, the offset weight is a distance coefficientof the candidate answer with respect to a keyword in the question indifferent candidate segments, and the question matching degree is asemantic matching degree between the candidate answer and the type ofthe opinion of the question.

In the above mentioned solution, the central control system 510 may bespecifically configured to:

filter the result of the search processing by using intra-domainknowledge; and

return the answer based on the result of the filtering.

As a preferable implementation, the central control system 510 may bespecifically configured to:

perform a second ranking operation of the result of the filtering; and

return the ranked result of the filtering.

Further, the central control system 510 may be specifically configuredto: rank the result of the filtering by using a supervised machinelearning system.

As another preferable implementation, the central control system 510 maybe specifically configured to:

perform a third ranking operation of the result of the filtering; and

return the ranked result of the filtering.

Further, the central control system 510 may be specifically configuredto:

validate a matching degree between the candidate answer and thequestion; and

rank the candidate answers based on the matching degree.

The system for implementing intelligent QA that is provided by thisembodiment of the present invention can perform the method forimplementing intelligent QA that is provided by any embodiment of thepresent invention, has corresponding functional modules for performingthe method, and has beneficial effects.

Sixth Embodiment

This embodiment provides a non-volatile computer storage medium. Thecomputer storage medium stores one or more modules which when executedby a device that performs a method for implementing intelligent QA,cause the device to perform the following operations:

receiving a query question;

performing a semantic analysis of the question;

performing corresponding search processing for the question based on aresult of the semantic analysis, wherein the search processing includessearch processing performed for the question by at least one of asemantic relationship mining system, a text library search system, aknowledge base search system, and a question and answer library searchsystem; and

returning an answer based on a result of the search processing.

When the modules stored in the above-mentioned storage medium areexecuted by the device, performing the semantic analysis of the questionmay preferably include:

identifying a focus, an opinion, and a type of the opinion of thequestion.

When the modules stored in the above-mentioned storage medium areexecuted by the device, the method may further include, after the typeof the opinion of the question is identified:

performing normalization processing on the type of the opinion of thequestion.

When the modules stored in the above-mentioned storage medium areexecuted by the device, performing the corresponding search processingfor the question based on the result of the semantic analysis maypreferably include:

performing search processing for the question by the semanticrelationship mining system if the analyzed question has a structuraldependency type; otherwise, performing search processing for thequestion by the text library search system, the knowledge base searchsystem, and the question and answer library search system.

When the modules stored in the above-mentioned storage medium areexecuted by the device, performing search processing for the question bythe semantic relationship mining system may preferably include:

searching a ternary relationship library for an answer to the question.

When the modules stored in the above-mentioned storage medium areexecuted by the device, the ternary relationship library may beestablished by:

performing a grammatical analysis of an original web page in a networkto obtain a sentence having a dependency relationship;

extracting nodes of the dependency relationship to obtain a ternaryrelationship triple; and

validating reasonableness of the ternary relationship triple, andestablishing the ternary relationship library by using the ternaryrelationship triple the reasonableness of which is validated.

When the modules stored in the above-mentioned storage medium areexecuted by the device, the method may further include, before thesentence having the dependency relationship is obtained:

removing content in brackets in the sentence.

When the modules stored in the above-mentioned storage medium areexecuted by the device, the method may further include, before theternary relationship triple is obtained:

adding at least one of a subject, an adverbial, and an object that thesentence having the dependency relationship lacks.

When the modules stored in the above-mentioned storage medium areexecuted by the device, performing the search processing for thequestion by the text library search system may preferably include:

performing a search to obtain a document set related to the question;

searching the document set for a candidate segment, wherein thecandidate segment include a candidate answer; and

extracting the candidate answer from the candidate segment, andperforming a first ranking operation.

When the modules stored in the above-mentioned storage medium areexecuted by the device, performing the first ranking operation mayinclude:

performing ranking based on at least one feature of a uniform resourcelocator (URL) weight, an offset weight, and a question matching degree,wherein the URL weight is a weight of a URL link of a site to which adocument where the candidate answer is located belongs, the offsetweight is a distance coefficient of the candidate answer with respect toa keyword in the question in different candidate segments, and thequestion matching degree is a syntactic matching degree between thecandidate answer and the type of the opinion of the question.

When the modules stored in the above-mentioned storage medium areexecuted by the device, returning the answer based on the result of thesearch processing may include:

filtering the result of the search processing by using intra-domainknowledge; and

returning the answer based on a result of the filtering.

When the modules stored in the above-mentioned storage medium areexecuted by the device, returning the answer based on the result of thefiltering may include:

performing a second ranking operation of the result of the filtering;and

returning the ranked result of the filtering.

When the modules stored in the above-mentioned storage medium areexecuted by the device, performing the second ranking operation of theresult of the filtering may include:

ranking the result of the filtering by using a supervised machinelearning model.

When the modules stored in the above-mentioned storage medium areexecuted by the device, returning the answer based on the result of thefiltering may include:

performing a third ranking operation of the result of the filtering; and

returning the ranked result of the filtering.

When the modules stored in the above-mentioned storage medium areexecuted by the device, performing the third ranking operation of theresult of the filtering may include:

validating a matching degree between the candidate answer and thequestion; and

ranking the candidate answer based on the matching degree.

Seventh Embodiment

FIG. 6 is a schematic structural diagram of hardware of a device forexecuting a method for implementing intelligent QA according to aseventh embodiment of the present invention.

The device includes:

one or more processors 610, and only one processor 610 is shown in FIG.6 as an example;

a memory 620; and one or more modules.

The device may further include: an input apparatus 630 and an outputapparatus 640. The processor 610, the memory 620, the input apparatus630, and the output apparatus 640 in the device may be connected by abus or in other manners. FIG. 6 uses bus connection as an example.

As a computer readable storage medium, the memory 620 may be configuredto store software programs, computer executable programs and modules,such as program instructions/modules corresponding to the method forimplementing intelligent QA in the embodiments of the present invention(for example, the central control system 510, the question analysissystem 520, and the back-end system 530 in the system for implementingintelligent QA shown in FIG. 5). The processor 610 executes the softwareprograms, instructions, and modules stored in the memory 620, so as toperform various functional applications and data processing of a server,i.e., implement the method for implementing intelligent QA in the abovementioned method embodiment.

The memory 620 may include a program storage area and a data storagearea. The program storage area may store an operating system and anapplication required by at least one function; the data storage area maystore data that is created based on the use of a terminal device, etc.In addition, the memory 620 may include a high speed random accessmemory, and may further include a non-volatile memory, for example, atleast one disk storage device, a flash memory device, or othernon-volatile solid-state storage device. In some examples, the memory620 may further include memories disposed remotely with respect to theprocessor 610, and these memories may be connected to the terminaldevice through a network. Examples of the above-mentioned networkinclude, but are not limited to, the Internet, an enterprise intranet, alocal area network, a mobile communication network, and combinationsthereof.

The input apparatus 630 may be configured to receive number or characterinformation that is input, and generate a key signal input related tothe user settings and functional control of the terminal. The outputapparatus 640 may include a display device such as a display screen.

The one or more modules are stored in the memory 620, and when executedby the one or more processors 610, perform the following operations:

receiving a query question;

performing a semantic analysis of the question;

performing corresponding search processing for the question based on aresult of the semantic analysis, wherein the search processing includessearch processing performed for the question by at least one of asemantic relationship mining system, a text library search system, aknowledge base search system, and a question and answer library searchsystem; and

returning an answer based on a result of the search processing.

Further, performing the semantic analysis of the question may include:

identifying a focus, an opinion, and a type of the opinion of thequestion.

Further, the method may further include, after the type of the opinionof the question is identified:

performing normalization processing on the type of the opinion of thequestion.

Further, performing the corresponding search processing for the questionbased on the result of the semantic analysis may include:

performing search processing for the question by the semanticrelationship mining system if the type of the analyzed question is astructural dependency type; otherwise, performing search processing forthe question by the text library search system, the knowledge basesearch system, and the question and answer library search system.

Further, performing the search processing for the question by thesemantic relationship mining system may include:

searching a ternary relationship library for an answer to the question.

Further, the ternary relationship library may be established by:

performing a grammatical analysis of an original web page in a networkto obtain a sentence having a dependency relationship;

extracting nodes of the dependency relationship to obtain a ternaryrelationship triple; and

validating reasonableness of the ternary relationship triple, andestablishing the ternary relationship library by using the ternaryrelationship triple the reasonableness of which is validated.

Further, the method may further include, before the sentence having thedependency relationship is obtained:

removing content in brackets in the sentence.

Further, the method may further include, before the ternary relationshiptriple is obtained:

adding at least one of a subject, an adverbial, and an object that thesentence having the dependency relationship lacks.

Further, performing the search processing for the question by the textlibrary search system may include:

performing a search to obtain a document set related to the question;

searching the document set for a candidate segment, wherein thecandidate segment includes a candidate answer; and

extracting the candidate answer from the candidate segment, andperforming a first ranking operation.

Further, performing the first ranking operation may include:

performing ranking based on at least one feature of a uniform resourcelocator (URL) weight, an offset weight, and a question matching degree,wherein the URL weight is a weight of a URL link of a site to which adocument where the candidate answer is located belongs, the offsetweight is a distance coefficient of the candidate answer with respect toa keyword in the question in different candidate segments, and thequestion matching degree is a syntactic matching degree between thecandidate answer and the type of the opinion of the question.

Further, returning the answer based on the result of the searchprocessing may include:

filtering the result of the search processing by using intra-domainknowledge; and

returning the answer based on a result of the filtering.

Further, returning the answer based on the result of the filtering mayinclude:

performing a second ranking operation of the result of the filtering;and

returning the ranked result of the filtering.

Further, performing the second ranking operation of the result of thefiltering may include:

ranking the result of the filtering by using a supervised machinelearning model.

Further, returning the answer based on the result of the filtering mayinclude:

performing a third ranking operation of the result of the filtering; and

returning the ranked result of the filtering.

Further, performing the third ranking operation of the result of thefiltering may include:

validating a matching degree between the candidate answer and thequestion; and

ranking the candidate answer based on the matching degree.

According to the foregoing description of the embodiments, it should beclear to those skilled in the art that the present invention may beimplemented by means of software and necessary general hardware, andcertainly may be implemented by hardware. In most cases, the formerimplementation is preferred. Based on such an understanding, thetechnical solutions in the present invention essentially, or the partcontributing to the prior art may be implemented in the form of asoftware product. The computer software product may be stored in acomputer readable storage medium, such as a floppy disk, a Read-OnlyMemory (ROM), a Random Access Memory (RAM), a flash, a magnetic disk, oran optical disk of a computer, and includes several instructions whichcauses a computer device (which may be a personal computer, a server, anetwork device, etc.) to perform the method described in the embodimentsof the present invention.

It should be noted that, in the above mentioned embodiments for thesystems for implementing intelligent QA, the systems are divided basedon functional logic only, but the present invention is not limited tothe above division as long as corresponding functions can beimplemented. In addition, the specific names of the functional units areused only for distinguishing one from another, but do not intend tolimit the scope of protection of the present invention.

Described above are merely exemplary embodiments of the presentinvention, but the protection scope of the present invention is notlimited thereto. Any variations or substitutions readily conceivable tothose skilled in the art within the disclosed technical scope of thepresent invention shall fall into the protection scope of the presentinvention. Accordingly, the protection scope of the present invention isdefined by the protection scope of the claims.

What is claimed is:
 1. A method for implementing intelligent questionanswering, comprising: receiving a query question; performing a semanticanalysis of the query question by analyzing the query question to obtaina dependency syntax tree that describes a dependency relationshipbetween various portions of the query question; performing correspondingsearch processing for the query question based on a result of thesemantic analysis, the corresponding search processing comprising searchprocessing performed for the query question by a text library searchsystem; and returning an answer based on a result of the correspondingsearch processing, wherein performing the search processing for thequery question by the text library search system comprises: performing asearch to obtain a document set related to the query question;calculating document correlation of documents in the document set;searching the document set for a candidate segment based on the documentcorrelation, the candidate segment comprising a candidate answer; andextracting the candidate answer from the candidate segment, andperforming a first ranking operation; wherein performing the firstranking operation comprises: performing ranking based on at least onefeature of a uniform resource locator (URL) weight, and an offsetweight, the URL weight being a weight of a URL link and a site to whicha document where the candidate answer is located belongs, and the offsetweight being a distance coefficient of the candidate answer with respectto a keyword in the query question in different candidate segments. 2.The method according to claim 1, wherein the performing the semanticanalysis of the query question comprises: identifying a focus, anopinion, and a type of the opinion of the query question.
 3. The methodaccording to claim 2, further comprising, after the type of the opinionof the query question is identified: performing normalization processingon the type of the opinion of the query question.
 4. The methodaccording to claim 1, wherein the returning the answer based on theresult of the corresponding search processing comprises: filtering theresult of the corresponding search processing by using intra-domainknowledge; and returning the answer based on a result of the filtering.5. The method according to claim 4, wherein the returning the answerbased on the result of the filtering comprises: performing a secondranking operation of the result of the filtering; and returning theranked result of the filtering.
 6. The method according to claim 5,wherein the performing the second ranking operation of the result of thefiltering comprises: ranking the result of the filtering by using asupervised machine learning model.
 7. The method according to claim 5,wherein the returning the answer based on the result of the filteringcomprises: performing a third ranking operation of the result of thefiltering; and returning the ranked result of the filtering.
 8. Themethod according to claim 7, wherein the performing the third rankingoperation of the result of the filtering comprises: validating amatching degree between the candidate answer and the query question; andranking the candidate answer based on the matching degree.
 9. A systemfor implementing intelligent question answering, comprising: at leastone processor; and a memory storing instructions, which when executed bythe at least one processor, cause the at least one processor to performoperations, the operations comprising: receiving a query question;performing a semantic analysis of the query question by analyzing thequery question to obtain a dependency syntax tree that describes adependency relationship between various portions of the query question;performing corresponding search processing for the query question basedon a result of the semantic analysis, the corresponding searchprocessing comprising search processing performed for the query questionby a text library search system; and returning an answer based on aresult of the corresponding search processing, wherein the performingthe search processing for the query question by the text library searchsystem comprises: performing a search to obtain a document set relatedto the query question; calculating document correlation of documents inthe document set; searching the document set for a candidate segmentbased on the document correlation, the candidate segment comprising acandidate answer; and extracting the candidate answer from the candidatesegment, and performing a first ranking operation; wherein performingthe first ranking operation comprises: performing ranking based on atleast one feature of a uniform resource locator (URL) weight, and anoffset weight, the URL weight being a weight of a URL link and a site towhich a document where the candidate answer is located belongs, and theoffset weight being a distance coefficient of the candidate answer withrespect to a keyword in the query question in different candidatesegments.
 10. The system according to claim 9, wherein performing thesemantic analysis of the query question analysis comprises: identifyinga focus, an opinion, and a type of the opinion of the query question.11. The system according to claim 10, wherein the operations furthercomprise: performing normalization processing on the type of the opinionof the query question after the type of the question is identified. 12.A non-volatile computer storage medium storing one or more modules, theone or more modules which when executed by a device, cause the device toperform a method for implementing intelligent question answering, themethod comprising: receiving a query question; performing a semanticanalysis of the query question by analyzing the query question to obtaina dependency syntax tree that describes a dependency relationshipbetween various portions of the query question; performing correspondingsearch processing for the query question based on a result of thesemantic analysis, the corresponding search processing comprising searchprocessing performed for the query question by a text library searchsystem; and returning an answer based on a result of the correspondingsearch processing, wherein the performing the search processing for thequery question by the text library search system comprises: performing asearch to obtain a document set related to the query question;calculating document correlation of documents in the document set;searching the document set for a candidate segment based on the documentcorrelation, the candidate segment comprising a candidate answer; andextracting the candidate answer from the candidate segment, andperforming a first ranking operation; wherein performing the firstranking operation comprises: performing ranking based on at least onefeature of a uniform resource locator (URL) weight, and an offsetweight, the URL weight being a weight of a URL link and a site to whicha document where the candidate answer is located belongs, and the offsetweight being a distance coefficient of the candidate answer with respectto a keyword in the query question in different candidate segments.